Through the use of dynamic and opportunistic spectrum access, cognitive radio (CR) enables high spectrum efficiency. The term cognitive radio was first proposed in late 1990s (see, J. Mitola and G. Q. Maquire, “Cognitive radio: making software radios more personal,” IEEE Personal Communications, August 1999) and a comprehensive overview has been provided in S. Haykin, “Cognitive radio: Brain-empowered wireless communications,” IEEE Journal on Selected Areas in Communications, vol. 23, No. 2, pp. 201-220, February 2005.
The basic concept of CR is to allow unlicensed CR users, also called secondary users, to use licensed spectrum bands (also referred to as licensed frequency bands) as long as they do not cause interference to licensed users, also called primary users. Therefore, CR users must be able to identify and use spectrum bands that are not being used by primary users. Several spectrum sensing techniques, such as matched filter detection, energy detection, feature detection, wavelet-based detection, and covariance-based detection, have been introduced to allow CR to identify a licensed signal within a spectrum band (see, D. Cabric, S. M. Mishra, and R. W. Brodersen, “Implementation issues in spectrum sensing for cognitive radios,” Proceedings of IEEE Asilomar Conference on Signals, Systems, and Computing, Pacific Grove, Calif., November 2004, pp. 772-776; H. Tang, “Some physical layer issues of wide-band cognitive radio systems,” Proceedings of IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Baltimore, Md., November 2005, pp. 151-159; Z. Tian and G. B. Giannakis, “A wavelet approach to wideband spectrum sensing for cognitive radios,” Proceedings of International Conference on Cognitive Radio Oriented Wireless Networks and Communications, Mykonos, Greece, June 2006, pp. 1-5; Y. Zeng and Y. C. Liang, “Maximum-minimum eigenvalue detection for cognitive radio,” Proceedings of IEEE International Symposium on Personal Indoor and Mobile Radio Communications, Athens, Greece, September 2007, pp. 1-5). Furthermore, cooperative spectrum sensing schemes have also been proposed to improve spectrum detection performance under fading and shadowing environments.
However, the efficiency of opportunistic spectrum sharing of a CR system relies not only on the performance of spectrum sensing techniques, but also on the scheduling of spectrum sensing activities (see, A. T. Hoang and Y.-C. Liang, “Adaptive scheduling of spectrum sensing periods in cognitive radio networks,” Proceedings of IEEE Global Telecommunications Conference, Washington, D.C., November 2007, pp. 3128-3132). If spectrum sensing activities are scheduled too often, the CR user may spend too much time on spectrum sensing, which is neither energy efficient nor bandwidth efficient. If spectrum sensing activities are seldom scheduled, a transmission made by a licensed user may not be quickly discovered, which may be harmful to licensed communications since a CR user may think that it may be free to transmit and cause interference with the transmission made by the licensed user.
In a periodic spectrum sensing framework, wherein a frame consists of a spectrum sensing block and an inter-sensing block, a ratio of spectrum sensing block length to inter-sensing block length represents how frequently spectrum sensing activities are scheduled, and determines the spectrum efficiency of the CR system, as well as the interference duration of the licensed system. Therefore, the ratio is a key parameter in spectrum sensing scheduling. Recently, optimizations of both the spectrum sensing and the inter-sensing block lengths have been studied using statistics of licensed spectrum band occupancy and spectrum sensing block length optimization has been investigated to improve bandwidth efficiency of a CR system over single and multiple licensed spectrum bands. However, a reappearance of licensed users (i.e., a transmission subsequent to a spectrum sensing activity) and possible detection errors have not been taken into consideration.